Overview

Dataset statistics

Number of variables15
Number of observations233615
Missing cells949884
Missing cells (%)27.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory26.7 MiB
Average record size in memory120.0 B

Variable types

Numeric12
Categorical3

Alerts

anio is highly overall correlated with dniHigh correlation
dni is highly overall correlated with anioHigh correlation
COMISION is highly overall correlated with AULA and 1 other fieldsHigh correlation
AULA is highly overall correlated with COMISION and 1 other fieldsHigh correlation
SEDE is highly overall correlated with COMISION and 1 other fieldsHigh correlation
pa1 is highly overall correlated with pa2High correlation
pa2 is highly overall correlated with pa1High correlation
codCarrera is highly overall correlated with facultadHigh correlation
MATERIA is highly overall correlated with facultadHigh correlation
facultad is highly overall correlated with codCarrera and 1 other fieldsHigh correlation
pa1 has 121874 (52.2%) missing valuesMissing
pa2 has 152308 (65.2%) missing valuesMissing
Final has 215187 (92.1%) missing valuesMissing
rem1 has 228508 (97.8%) missing valuesMissing
rem2 has 232007 (99.3%) missing valuesMissing
pa1 has 8943 (3.8%) zerosZeros
pa2 has 4007 (1.7%) zerosZeros

Reproduction

Analysis started2023-07-30 21:55:57.257400
Analysis finished2023-07-30 21:56:21.964914
Duration24.71 seconds
Software versionydata-profiling vv4.1.2
Download configurationconfig.json

Variables

anio
Real number (ℝ)

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2015.4922
Minimum2011
Maximum2019
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2023-07-30T18:56:22.023829image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2011
5-th percentile2011
Q12013
median2016
Q32018
95-th percentile2019
Maximum2019
Range8
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.5535384
Coefficient of variation (CV)0.0012669552
Kurtosis-1.1183604
Mean2015.4922
Median Absolute Deviation (MAD)2
Skewness-0.26797939
Sum4.7084922 Ă— 108
Variance6.5205582
MonotonicityIncreasing
2023-07-30T18:56:22.163157image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2019 33352
14.3%
2018 31966
13.7%
2017 30334
13.0%
2016 28442
12.2%
2015 27164
11.6%
2014 22162
9.5%
2011 20751
8.9%
2013 20076
8.6%
2012 19368
8.3%
ValueCountFrequency (%)
2011 20751
8.9%
2012 19368
8.3%
2013 20076
8.6%
2014 22162
9.5%
2015 27164
11.6%
2016 28442
12.2%
2017 30334
13.0%
2018 31966
13.7%
2019 33352
14.3%
ValueCountFrequency (%)
2019 33352
14.3%
2018 31966
13.7%
2017 30334
13.0%
2016 28442
12.2%
2015 27164
11.6%
2014 22162
9.5%
2013 20076
8.6%
2012 19368
8.3%
2011 20751
8.9%

cuat
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
2
125228 
1
108387 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters233615
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
2 125228
53.6%
1 108387
46.4%

Length

2023-07-30T18:56:22.294864image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-30T18:56:22.416412image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
2 125228
53.6%
1 108387
46.4%

Most occurring characters

ValueCountFrequency (%)
2 125228
53.6%
1 108387
46.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 233615
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 125228
53.6%
1 108387
46.4%

Most occurring scripts

ValueCountFrequency (%)
Common 233615
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 125228
53.6%
1 108387
46.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 233615
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 125228
53.6%
1 108387
46.4%

dni
Real number (ℝ)

Distinct165535
Distinct (%)70.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46539530
Minimum8
Maximum99999999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2023-07-30T18:56:22.538188image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile33076700
Q137558154
median39802418
Q342103330
95-th percentile95826572
Maximum99999999
Range99999991
Interquartile range (IQR)4545176.5

Descriptive statistics

Standard deviation20082000
Coefficient of variation (CV)0.43150414
Kurtosis2.240563
Mean46539530
Median Absolute Deviation (MAD)2283682
Skewness1.9663044
Sum1.0872332 Ă— 1013
Variance4.0328671 Ă— 1014
MonotonicityNot monotonic
2023-07-30T18:56:22.689998image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26797 15
 
< 0.1%
29064815 13
 
< 0.1%
31929046 13
 
< 0.1%
17262890 12
 
< 0.1%
26730 12
 
< 0.1%
26542541 11
 
< 0.1%
26542873 11
 
< 0.1%
24296824 11
 
< 0.1%
26756 10
 
< 0.1%
16874194 10
 
< 0.1%
Other values (165525) 233497
99.9%
ValueCountFrequency (%)
8 1
 
< 0.1%
40 1
 
< 0.1%
53 3
< 0.1%
218 1
 
< 0.1%
652 1
 
< 0.1%
1259 2
< 0.1%
1603 1
 
< 0.1%
2319 1
 
< 0.1%
4565 1
 
< 0.1%
5041 1
 
< 0.1%
ValueCountFrequency (%)
99999999 1
< 0.1%
99964945 1
< 0.1%
99902118 1
< 0.1%
99527525 1
< 0.1%
99127183 1
< 0.1%
99074664 1
< 0.1%
99059954 1
< 0.1%
99053452 1
< 0.1%
99053449 1
< 0.1%
99053442 1
< 0.1%

COMISION
Real number (ℝ)

Distinct269
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean77126.003
Minimum10301
Maximum425302
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2023-07-30T18:56:22.845821image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum10301
5-th percentile15302
Q125303
median45315
Q3105302
95-th percentile325302
Maximum425302
Range415001
Interquartile range (IQR)79999

Descriptive statistics

Standard deviation87746.271
Coefficient of variation (CV)1.1377002
Kurtosis4.8225331
Mean77126.003
Median Absolute Deviation (MAD)25005
Skewness2.308688
Sum1.8017791 Ă— 1010
Variance7.6994081 Ă— 109
MonotonicityNot monotonic
2023-07-30T18:56:22.983520image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
105302 2562
 
1.1%
105301 2481
 
1.1%
20302 2271
 
1.0%
105306 2255
 
1.0%
20304 2243
 
1.0%
105305 2235
 
1.0%
105304 2187
 
0.9%
105303 2127
 
0.9%
25306 2121
 
0.9%
65301 2116
 
0.9%
Other values (259) 211017
90.3%
ValueCountFrequency (%)
10301 1790
0.8%
10302 1511
0.6%
10303 1430
0.6%
10304 1275
0.5%
10305 971
0.4%
10306 850
0.4%
10307 293
 
0.1%
10308 325
 
0.1%
10309 279
 
0.1%
10310 225
 
0.1%
ValueCountFrequency (%)
425302 240
 
0.1%
425301 559
0.2%
415303 86
 
< 0.1%
415302 449
0.2%
415301 442
0.2%
395304 92
 
< 0.1%
395303 685
0.3%
395302 881
0.4%
395301 1003
0.4%
385303 47
 
< 0.1%

HORARIO
Real number (ℝ)

Distinct64
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean646.06246
Minimum201
Maximum696
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2023-07-30T18:56:23.126514image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum201
5-th percentile621
Q1628
median656
Q3658
95-th percentile682
Maximum696
Range495
Interquartile range (IQR)30

Descriptive statistics

Standard deviation50.911368
Coefficient of variation (CV)0.078802548
Kurtosis40.07491
Mean646.06246
Median Absolute Deviation (MAD)11
Skewness-5.6839043
Sum1.5092988 Ă— 108
Variance2591.9674
MonotonicityNot monotonic
2023-07-30T18:56:23.273529image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
656 27634
11.8%
681 27050
11.6%
682 26280
11.2%
652 24618
10.5%
657 22106
9.5%
651 22088
9.5%
658 15272
6.5%
626 13075
 
5.6%
622 10570
 
4.5%
627 10514
 
4.5%
Other values (54) 34408
14.7%
ValueCountFrequency (%)
201 36
 
< 0.1%
204 54
 
< 0.1%
205 195
 
0.1%
213 817
0.3%
234 66
 
< 0.1%
235 32
 
< 0.1%
244 247
 
0.1%
245 23
 
< 0.1%
261 8
 
< 0.1%
262 115
 
< 0.1%
ValueCountFrequency (%)
696 140
 
0.1%
694 5
 
< 0.1%
693 37
 
< 0.1%
692 19
 
< 0.1%
682 26280
11.2%
681 27050
11.6%
662 101
 
< 0.1%
658 15272
6.5%
657 22106
9.5%
656 27634
11.8%

AULA
Real number (ℝ)

Distinct68
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.705032
Minimum1
Maximum215
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2023-07-30T18:56:23.421100image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q110
median26
Q3102
95-th percentile214
Maximum215
Range214
Interquartile range (IQR)92

Descriptive statistics

Standard deviation77.793135
Coefficient of variation (CV)1.2406203
Kurtosis-0.30680999
Mean62.705032
Median Absolute Deviation (MAD)19
Skewness1.2014522
Sum14648836
Variance6051.7718
MonotonicityNot monotonic
2023-07-30T18:56:23.564048image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 15845
 
6.8%
13 10557
 
4.5%
214 10371
 
4.4%
33 8843
 
3.8%
12 8335
 
3.6%
15 7871
 
3.4%
201 7550
 
3.2%
4 7327
 
3.1%
45 6923
 
3.0%
32 6813
 
2.9%
Other values (58) 143180
61.3%
ValueCountFrequency (%)
1 15845
6.8%
2 6658
2.8%
3 4502
 
1.9%
4 7327
3.1%
5 4629
 
2.0%
6 4893
 
2.1%
7 5287
 
2.3%
8 1969
 
0.8%
9 2421
 
1.0%
10 4885
 
2.1%
ValueCountFrequency (%)
215 1986
 
0.9%
214 10371
4.4%
213 3864
 
1.7%
212 2982
 
1.3%
211 3943
 
1.7%
210 1079
 
0.5%
209 2413
 
1.0%
208 3041
 
1.3%
207 3715
 
1.6%
206 3545
 
1.5%

SEDE
Real number (ℝ)

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.3239732
Minimum1
Maximum42
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2023-07-30T18:56:23.692208image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q310
95-th percentile32
Maximum42
Range41
Interquartile range (IQR)8

Descriptive statistics

Standard deviation8.7504274
Coefficient of variation (CV)1.1947651
Kurtosis4.8117085
Mean7.3239732
Median Absolute Deviation (MAD)2
Skewness2.3096073
Sum1710990
Variance76.569979
MonotonicityNot monotonic
2023-07-30T18:56:23.798256image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
2 50087
21.4%
10 36149
15.5%
4 35809
15.3%
1 34616
14.8%
5 25840
11.1%
6 20247
8.7%
15 7203
 
3.1%
33 4092
 
1.8%
32 3872
 
1.7%
28 3569
 
1.5%
Other values (14) 12131
 
5.2%
ValueCountFrequency (%)
1 34616
14.8%
2 50087
21.4%
4 35809
15.3%
5 25840
11.1%
6 20247
8.7%
10 36149
15.5%
13 84
 
< 0.1%
14 3088
 
1.3%
15 7203
 
3.1%
21 1614
 
0.7%
ValueCountFrequency (%)
42 799
 
0.3%
41 977
 
0.4%
39 2661
1.1%
38 653
 
0.3%
37 70
 
< 0.1%
36 5
 
< 0.1%
35 401
 
0.2%
34 892
 
0.4%
33 4092
1.8%
32 3872
1.7%

MATERIA
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
53
167251 
3
66364 

Length

Max length2
Median length2
Mean length1.7159258
Min length1

Characters and Unicode

Total characters400866
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
53 167251
71.6%
3 66364
 
28.4%

Length

2023-07-30T18:56:23.914485image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-30T18:56:24.194981image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
53 167251
71.6%
3 66364
 
28.4%

Most occurring characters

ValueCountFrequency (%)
3 233615
58.3%
5 167251
41.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 400866
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 233615
58.3%
5 167251
41.7%

Most occurring scripts

ValueCountFrequency (%)
Common 400866
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 233615
58.3%
5 167251
41.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 400866
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 233615
58.3%
5 167251
41.7%

pa1
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct21
Distinct (%)< 0.1%
Missing121874
Missing (%)52.2%
Infinite0
Infinite (%)0.0%
Mean3.6401545
Minimum0
Maximum10
Zeros8943
Zeros (%)3.8%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2023-07-30T18:56:24.286244image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q35
95-th percentile8
Maximum10
Range10
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.4903277
Coefficient of variation (CV)0.68412693
Kurtosis-0.48630176
Mean3.6401545
Median Absolute Deviation (MAD)2
Skewness0.52682409
Sum406754.5
Variance6.2017321
MonotonicityNot monotonic
2023-07-30T18:56:24.403092image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
1 16748
 
7.2%
2 16565
 
7.1%
4 16366
 
7.0%
3 14386
 
6.2%
5 10485
 
4.5%
0 8943
 
3.8%
6 8518
 
3.6%
7 6616
 
2.8%
8 4780
 
2.0%
9 3106
 
1.3%
Other values (11) 5228
 
2.2%
(Missing) 121874
52.2%
ValueCountFrequency (%)
0 8943
3.8%
0.5 88
 
< 0.1%
1 16748
7.2%
1.5 389
 
0.2%
2 16565
7.1%
2.5 585
 
0.3%
3 14386
6.2%
3.5 546
 
0.2%
4 16366
7.0%
4.5 600
 
0.3%
ValueCountFrequency (%)
10 1541
 
0.7%
9.5 96
 
< 0.1%
9 3106
 
1.3%
8.5 203
 
0.1%
8 4780
2.0%
7.5 294
 
0.1%
7 6616
2.8%
6.5 389
 
0.2%
6 8518
3.6%
5.5 497
 
0.2%

pa2
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct22
Distinct (%)< 0.1%
Missing152308
Missing (%)65.2%
Infinite0
Infinite (%)0.0%
Mean4.2868056
Minimum0
Maximum10
Zeros4007
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2023-07-30T18:56:24.522622image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q36
95-th percentile9
Maximum10
Range10
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.5293339
Coefficient of variation (CV)0.59002768
Kurtosis-0.70955882
Mean4.2868056
Median Absolute Deviation (MAD)2
Skewness0.28035647
Sum348547.3
Variance6.3975301
MonotonicityNot monotonic
2023-07-30T18:56:24.638799image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
4 13826
 
5.9%
2 10029
 
4.3%
3 9340
 
4.0%
5 9166
 
3.9%
1 8410
 
3.6%
6 7985
 
3.4%
7 6676
 
2.9%
8 5050
 
2.2%
0 4007
 
1.7%
9 3389
 
1.5%
Other values (12) 3429
 
1.5%
(Missing) 152308
65.2%
ValueCountFrequency (%)
0 4007
 
1.7%
0.5 38
 
< 0.1%
1 8410
3.6%
1.5 147
 
0.1%
2 10029
4.3%
2.5 254
 
0.1%
3 9340
4.0%
3.5 176
 
0.1%
4 13826
5.9%
4.5 303
 
0.1%
ValueCountFrequency (%)
10 1707
 
0.7%
9.5 39
 
< 0.1%
9 3389
1.5%
8.5 93
 
< 0.1%
8 5050
2.2%
7.8 1
 
< 0.1%
7.5 181
 
0.1%
7 6676
2.9%
6.5 237
 
0.1%
6 7985
3.4%

Final
Real number (ℝ)

Distinct11
Distinct (%)0.1%
Missing215187
Missing (%)92.1%
Infinite0
Infinite (%)0.0%
Mean4.0492728
Minimum0
Maximum10
Zeros81
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2023-07-30T18:56:24.754526image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q36
95-th percentile8
Maximum10
Range10
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.1022495
Coefficient of variation (CV)0.51916717
Kurtosis-0.46228877
Mean4.0492728
Median Absolute Deviation (MAD)2
Skewness0.52160514
Sum74620
Variance4.419453
MonotonicityNot monotonic
2023-07-30T18:56:24.860456image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
2 3793
 
1.6%
3 3161
 
1.4%
4 2964
 
1.3%
5 2380
 
1.0%
6 1897
 
0.8%
7 1467
 
0.6%
1 1420
 
0.6%
8 774
 
0.3%
9 378
 
0.2%
10 113
 
< 0.1%
(Missing) 215187
92.1%
ValueCountFrequency (%)
0 81
 
< 0.1%
1 1420
 
0.6%
2 3793
1.6%
3 3161
1.4%
4 2964
1.3%
5 2380
1.0%
6 1897
0.8%
7 1467
 
0.6%
8 774
 
0.3%
9 378
 
0.2%
ValueCountFrequency (%)
10 113
 
< 0.1%
9 378
 
0.2%
8 774
 
0.3%
7 1467
 
0.6%
6 1897
0.8%
5 2380
1.0%
4 2964
1.3%
3 3161
1.4%
2 3793
1.6%
1 1420
 
0.6%

codCarrera
Real number (ℝ)

Distinct92
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean120.16956
Minimum1
Maximum999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2023-07-30T18:56:24.998062image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9
Q130
median39
Q345
95-th percentile999
Maximum999
Range998
Interquartile range (IQR)15

Descriptive statistics

Standard deviation263.68459
Coefficient of variation (CV)2.194271
Kurtosis7.1110757
Mean120.16956
Median Absolute Deviation (MAD)7
Skewness2.9992463
Sum28073412
Variance69529.561
MonotonicityNot monotonic
2023-07-30T18:56:25.142194image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39 71812
30.7%
999 19122
 
8.2%
45 14238
 
6.1%
40 11054
 
4.7%
33 9408
 
4.0%
90 9340
 
4.0%
99 8639
 
3.7%
41 8595
 
3.7%
9 7711
 
3.3%
11 6460
 
2.8%
Other values (82) 67236
28.8%
ValueCountFrequency (%)
1 504
 
0.2%
2 53
 
< 0.1%
4 3658
1.6%
5 3363
1.4%
6 54
 
< 0.1%
7 380
 
0.2%
8 9
 
< 0.1%
9 7711
3.3%
10 8
 
< 0.1%
11 6460
2.8%
ValueCountFrequency (%)
999 19122
8.2%
140 75
 
< 0.1%
138 3
 
< 0.1%
137 15
 
< 0.1%
136 1
 
< 0.1%
135 298
 
0.1%
134 49
 
< 0.1%
133 731
 
0.3%
132 4605
 
2.0%
131 30
 
< 0.1%

facultad
Categorical

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
MEDICINA
104127 
INGENIERIA
36383 
CS EXACTAS Y NATURALES
19970 
999.0
19122 
CIENCIAS VETERINARIAS
14285 
Other values (10)
39728 

Length

Max length24
Median length22
Mean length10.875273
Min length4

Characters and Unicode

Total characters2540627
Distinct characters26
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowINGENIERIA
2nd rowCS EXACTAS Y NATURALES
3rd rowINGENIERIA
4th rowINGENIERIA
5th rowINGENIERIA

Common Values

ValueCountFrequency (%)
MEDICINA 104127
44.6%
INGENIERIA 36383
 
15.6%
CS EXACTAS Y NATURALES 19970
 
8.5%
999.0 19122
 
8.2%
CIENCIAS VETERINARIAS 14285
 
6.1%
FARMACIA Y BIOQUIMICA 12047
 
5.2%
99.0 8639
 
3.7%
ODONTOLOGIA 8595
 
3.7%
AGRONOMIA 6124
 
2.6%
CIENCIAS ECONOMICAS 1038
 
0.4%
Other values (5) 3285
 
1.4%

Length

2023-07-30T18:56:25.284710image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
medicina 104127
31.0%
ingenieria 36383
 
10.8%
y 33509
 
10.0%
cs 19970
 
5.9%
exactas 19970
 
5.9%
naturales 19970
 
5.9%
999.0 19122
 
5.7%
ciencias 15746
 
4.7%
veterinarias 14285
 
4.2%
farmacia 12047
 
3.6%
Other values (12) 41220
 
12.3%

Most occurring characters

ValueCountFrequency (%)
I 446395
17.6%
A 339957
13.4%
E 265264
10.4%
N 243637
9.6%
C 204508
8.0%
M 136369
 
5.4%
D 113303
 
4.5%
R 107139
 
4.2%
102734
 
4.0%
S 94612
 
3.7%
Other values (16) 486709
19.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2307727
90.8%
Space Separator 102734
 
4.0%
Decimal Number 102405
 
4.0%
Other Punctuation 27761
 
1.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I 446395
19.3%
A 339957
14.7%
E 265264
11.5%
N 243637
10.6%
C 204508
8.9%
M 136369
 
5.9%
D 113303
 
4.9%
R 107139
 
4.6%
S 94612
 
4.1%
O 65331
 
2.8%
Other values (12) 291212
12.6%
Decimal Number
ValueCountFrequency (%)
9 74644
72.9%
0 27761
 
27.1%
Space Separator
ValueCountFrequency (%)
102734
100.0%
Other Punctuation
ValueCountFrequency (%)
. 27761
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2307727
90.8%
Common 232900
 
9.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
I 446395
19.3%
A 339957
14.7%
E 265264
11.5%
N 243637
10.6%
C 204508
8.9%
M 136369
 
5.9%
D 113303
 
4.9%
R 107139
 
4.6%
S 94612
 
4.1%
O 65331
 
2.8%
Other values (12) 291212
12.6%
Common
ValueCountFrequency (%)
102734
44.1%
9 74644
32.0%
. 27761
 
11.9%
0 27761
 
11.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2540627
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I 446395
17.6%
A 339957
13.4%
E 265264
10.4%
N 243637
9.6%
C 204508
8.0%
M 136369
 
5.4%
D 113303
 
4.5%
R 107139
 
4.2%
102734
 
4.0%
S 94612
 
3.7%
Other values (16) 486709
19.2%

rem1
Real number (ℝ)

Distinct11
Distinct (%)0.2%
Missing228508
Missing (%)97.8%
Infinite0
Infinite (%)0.0%
Mean4.0569806
Minimum0
Maximum10
Zeros20
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2023-07-30T18:56:25.404575image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q36
95-th percentile8
Maximum10
Range10
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.0898548
Coefficient of variation (CV)0.51512564
Kurtosis-0.45301489
Mean4.0569806
Median Absolute Deviation (MAD)2
Skewness0.51890656
Sum20719
Variance4.3674929
MonotonicityNot monotonic
2023-07-30T18:56:25.507881image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
2 1066
 
0.5%
4 854
 
0.4%
3 841
 
0.4%
5 668
 
0.3%
6 543
 
0.2%
7 380
 
0.2%
1 379
 
0.2%
8 222
 
0.1%
9 105
 
< 0.1%
10 29
 
< 0.1%
(Missing) 228508
97.8%
ValueCountFrequency (%)
0 20
 
< 0.1%
1 379
 
0.2%
2 1066
0.5%
3 841
0.4%
4 854
0.4%
5 668
0.3%
6 543
0.2%
7 380
 
0.2%
8 222
 
0.1%
9 105
 
< 0.1%
ValueCountFrequency (%)
10 29
 
< 0.1%
9 105
 
< 0.1%
8 222
 
0.1%
7 380
 
0.2%
6 543
0.2%
5 668
0.3%
4 854
0.4%
3 841
0.4%
2 1066
0.5%
1 379
 
0.2%

rem2
Real number (ℝ)

Distinct11
Distinct (%)0.7%
Missing232007
Missing (%)99.3%
Infinite0
Infinite (%)0.0%
Mean3.7723881
Minimum0
Maximum10
Zeros11
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2023-07-30T18:56:25.609427image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q35
95-th percentile7
Maximum10
Range10
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.0425916
Coefficient of variation (CV)0.54145851
Kurtosis-0.44937404
Mean3.7723881
Median Absolute Deviation (MAD)1
Skewness0.5299905
Sum6066
Variance4.1721805
MonotonicityNot monotonic
2023-07-30T18:56:25.709841image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
2 381
 
0.2%
3 245
 
0.1%
4 236
 
0.1%
5 221
 
0.1%
1 170
 
0.1%
6 164
 
0.1%
7 105
 
< 0.1%
8 50
 
< 0.1%
9 19
 
< 0.1%
0 11
 
< 0.1%
(Missing) 232007
99.3%
ValueCountFrequency (%)
0 11
 
< 0.1%
1 170
0.1%
2 381
0.2%
3 245
0.1%
4 236
0.1%
5 221
0.1%
6 164
0.1%
7 105
 
< 0.1%
8 50
 
< 0.1%
9 19
 
< 0.1%
ValueCountFrequency (%)
10 6
 
< 0.1%
9 19
 
< 0.1%
8 50
 
< 0.1%
7 105
 
< 0.1%
6 164
0.1%
5 221
0.1%
4 236
0.1%
3 245
0.1%
2 381
0.2%
1 170
0.1%

Interactions

2023-07-30T18:56:19.242531image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:02.268516image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:04.022563image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:05.528661image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:07.004096image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:08.574176image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:10.116822image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:11.758374image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:13.226811image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:14.663739image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:16.139987image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:17.715368image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:19.370274image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:02.431243image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:04.173743image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:05.674730image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:07.152877image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:08.717063image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:10.279880image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:11.904428image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:13.357893image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:14.792462image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:16.290056image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:17.842717image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:19.485030image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:02.576278image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:04.302959image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:05.801780image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:07.308200image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:08.858867image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:10.562998image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:12.032745image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:13.480758image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:14.916035image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:16.438176image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:17.954800image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:19.608470image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:02.813151image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:04.433763image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:05.921471image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:07.434243image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:08.983556image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:10.684049image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:12.154164image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:13.596451image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:15.032542image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:16.564487image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:18.224955image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:19.732985image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:02.955732image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:04.565188image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:06.048015image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:07.569852image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:09.130682image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:10.810086image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:12.273936image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:13.730837image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:15.156501image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:16.713029image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:18.335150image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:19.841305image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:03.128418image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:04.691487image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:06.170776image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:07.705383image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:09.262565image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:10.932072image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:12.392903image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:13.849293image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:15.266275image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:16.853266image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:18.442994image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:19.968427image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:03.250496image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:04.812309image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:06.283100image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:07.825883image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:09.382585image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:11.047633image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:12.519833image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:13.959292image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:15.377953image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:16.982868image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:18.546690image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:20.090205image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:03.381757image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:04.929977image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:06.402425image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:07.952994image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:09.520235image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:11.158962image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:12.649019image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:14.082144image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:15.519778image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:17.102916image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:18.656412image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:20.202594image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:03.500410image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:05.044726image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:06.515099image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:08.067804image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:09.629920image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:11.273856image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:12.762676image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:14.199135image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:15.629572image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:17.221253image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:18.762241image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:20.325468image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:03.634766image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:05.171528image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:06.645383image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:08.201683image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:09.760211image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:11.405760image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:12.879807image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:14.326196image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:15.743709image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:17.366597image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:18.898460image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:20.438971image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:03.759630image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:05.287751image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:06.757324image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:08.319679image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:09.872080image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:11.520359image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:12.994193image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:14.437668image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:15.860921image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:17.484488image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:19.013012image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:20.558628image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:03.882547image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:05.400503image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:06.867301image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:08.437787image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:09.979269image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:11.633256image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:13.100802image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:14.551377image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:15.981400image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:17.600784image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T18:56:19.125859image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-07-30T18:56:25.819907image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
aniodniCOMISIONHORARIOAULASEDEpa1pa2FinalcodCarrerarem1rem2cuatMATERIAfacultad
anio1.0000.5700.090-0.034-0.0350.069-0.115-0.045-0.0580.231-0.179-0.1010.0580.1600.326
dni0.5701.0000.026-0.0070.0550.010-0.075-0.0310.0200.159-0.088-0.0770.0700.0860.131
COMISION0.0900.0261.000-0.341-0.5160.989-0.0120.054-0.0460.118-0.028-0.0560.1810.3360.158
HORARIO-0.034-0.007-0.3411.0000.251-0.3950.026-0.0360.048-0.0510.0240.0230.0830.1350.078
AULA-0.0350.055-0.5160.2511.000-0.5200.0420.0060.053-0.0840.058-0.0040.1610.3190.166
SEDE0.0690.0100.989-0.395-0.5201.000-0.0010.054-0.0590.088-0.045-0.0310.1800.3080.151
pa1-0.115-0.075-0.0120.0260.042-0.0011.0000.5760.172-0.0710.0860.0400.0480.1430.060
pa2-0.045-0.0310.054-0.0360.0060.0540.5761.0000.248-0.0100.1030.0670.0710.0520.043
Final-0.0580.020-0.0460.0480.053-0.0590.1720.2481.000-0.0180.1490.1020.1160.0330.023
codCarrera0.2310.1590.118-0.051-0.0840.088-0.071-0.010-0.0181.0000.0060.0140.0410.0580.714
rem1-0.179-0.088-0.0280.0240.058-0.0450.0860.1030.1490.0061.0000.1380.0770.0000.017
rem2-0.101-0.077-0.0560.023-0.004-0.0310.0400.0670.1020.0140.1381.0000.1430.0910.074
cuat0.0580.0700.1810.0830.1610.1800.0480.0710.1160.0410.0770.1431.0000.0620.209
MATERIA0.1600.0860.3360.1350.3190.3080.1430.0520.0330.0580.0000.0910.0621.0000.896
facultad0.3260.1310.1580.0780.1660.1510.0600.0430.0230.7140.0170.0740.2090.8961.000

Missing values

2023-07-30T18:56:20.756597image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-30T18:56:21.191310image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-07-30T18:56:21.792241image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

aniocuatdniCOMISIONHORARIOAULASEDEMATERIApa1pa2FinalcodCarrerafacultadrem1rem2
0201113654252810301.0651.045.0132.0NaNNaN26INGENIERIANaNNaN
1201113682143010301.0651.045.013NaNNaNNaN11CS EXACTAS Y NATURALESNaNNaN
2201113668480310301.0651.045.0138.07.0NaN29INGENIERIANaNNaN
3201113612809210301.0651.045.0134.04.03.029INGENIERIANaNNaN
4201113846543310301.0651.045.013NaNNaNNaN28INGENIERIANaNNaN
5201113703456110301.0651.045.013NaNNaNNaN39MEDICINANaNNaN
6201113700761310301.0651.045.0135.04.05.011CS EXACTAS Y NATURALESNaNNaN
7201113692973410301.0651.045.0134.03.0NaN14CS EXACTAS Y NATURALESNaNNaN
8201113533713310301.0651.045.0137.02.01.011CS EXACTAS Y NATURALESNaNNaN
9201113727616810301.0651.045.013NaNNaNNaN11CS EXACTAS Y NATURALESNaNNaN
aniocuatdniCOMISIONHORARIOAULASEDEMATERIApa1pa2FinalcodCarrerafacultadrem1rem2
233605201924324000045312.0652.037.0453NaNNaNNaN39MEDICINANaNNaN
233606201924331169445311.0652.015.0453NaNNaNNaN45CIENCIAS VETERINARIASNaNNaN
233607201924309298860303.0681.026.0634.04.5NaN30INGENIERIANaNNaN
233608201924294344845307.0651.015.0453NaNNaNNaN39MEDICINANaNNaN
233609201924243617420307.0681.0112.0233.02.0NaN14CS EXACTAS Y NATURALESNaNNaN
2336102019242284343105307.0627.01.010530.01.0NaN39MEDICINANaNNaN
233611201923945813355301.0621.04.05533.0NaNNaN14CS EXACTAS Y NATURALESNaNNaN
2336122019243034003105301.0621.039.010532.0NaNNaN39MEDICINANaNNaN
2336132019242841987105320.0682.023.01053NaNNaNNaN39MEDICINANaNNaN
233614201924078455825308.0656.0206.02534.01.0NaN39MEDICINANaNNaN